I’ve found that every once in a while, it doesn’t hurt to see what everyone else is doing in the same technology space I’m currently focused on. For the past 18 months, that’s been the Generative AI space and the impending implementation of Agentic AI across diverse industries and applications.
Getting to Generative AI: Like Learning a New Foreign Language
Full disclosure: I’m an Oracle DBA with 25 years of experience in data engineering and 45 years of experience in application programming. Lately I’ve focused on building out simple Generative AI and Retrieval Augmented Generation (RAG) chatbot applications with Oracle Database 23ai technology and Oracle Application Express (APEX) within the Oracle Cloud Infrastructure (OCI) public cloud. That meant learning how to use LLMs to chunk and create embeddings for a corpus of documents, how to perform cosine similarity searches against vectorized content, and prepare appropriate proper system prompts within a chatbot framework to return cogent answers from that corpus based on questions asked.
This was a decidedly non-trivial task – it took me several weeks to master these concepts and then build demos that yielded relatively hallucination-free answers, and it was at least two months before I felt I could comfortably present my work to colleagues at user group conferences. I came away with a new respect for the depth of knowledge required to deliver qualified answers from LLMs and Generative AI applications.
Qlik Connect 2025: Qlik Answers
Back in May 2025 I had a chance to take a close look at the latest version of Qlik Answers for developing Generative AI solutions. While at Qlik Connect, I spoke with executives and developers about their vision for capturing valuable business insights into their customers’ data, especially if it was unstructured information strewn across thousands of pages of documents.
The folks at Qlik granted me a trial account and I dove into what Qlik Answers could achieve. I was pleasantly surprised that it was a relatively straightforward path to construct a chatbot that could search through several hundred pages of documents from multiple sources – scholarly papers, digital news reports, blog posts by reputable authors – to return cogent answers to business questions.
What impressed me was how quickly this all came together: Importing my corpus of nearly 30 documents, indexing them for use, and constructing a basic chatbot that could chew on the corpus to provide answers took less than 15 minutes.
Handling Outliers Is What Matters
I’m not some starry-eyed dreamer about AI capabilities; indeed, my earlier work with generative AI had yielded some surprising confabulations depending on what questions I asked of my chatbot.
Thus my evaluations included some tricks I’d learned during my prior experiences – things like prompt injection attempts and even hiding system prompt overrides within a source document. I discovered that Qlik Answers was able to handle the twists I threw at it quite well without any additional fine-tuning.
Over to You: Have An In-Depth Look At Qlik Answers
Obviously, this brief blog post isn’t going to convince anyone of how well Qlik Answers performed during this process, so please have a look at my complete evaluation of this tool. It contains detailed screenshots and explanations of every step I took, including references to each document I used as a source for my corpus so you can quickly run similar evaluations. Of course, please feel free to post comments to let me what you have discovered.
